We present SeedProteo, a diffusion-based model for de novo all-atom protein design. We demonstrate how to repurpose a cutting-edge folding architecture into a powerful generative design framework by effectively integrating self-conditioning features. Extensive benchmarks highlight the model's capabilities across two distinct tasks: in unconditional generation, SeedProteo exhibits superior length generalization and structural diversity, maintaining robustness for long sequences and complex topologies; in binder design, it achieves state-of-the-art performance among open-source methods, attaining the highest in-silico design success rates, structural diversity and novelty. Finally, we validate SeedProteo through wet-lab assays on two therapeutic targets, achieving hit rates of 70%-80% and picomolar-level binding affinities, establishing leading results. To facilitate community adoption, we provide public access to SeedProteo via a webserver (https://seedfold.io/proteinDesign).
@article{arxiv.2512.24192,
title = {SeedProteo: Accurate De Novo All-Atom Design of Protein Binders},
author = {Wei Qu and Yiming Ma and Fei Ye and Chan Lu and Yi Zhou and Kexin Zhang and Lan Wang and Minrui Gui and Quanquan Gu},
journal= {arXiv preprint arXiv:2512.24192},
year = {2026}
}